Training text recognition systems

ABSTRACT

In implementations of recognizing text in images, text recognition systems are trained using noisy images that have nuisance factors applied, and corresponding clean images (e.g., without nuisance factors). Clean images serve as supervision at both feature and pixel levels, so that text recognition systems are trained to be feature invariant (e.g., by requiring features extracted from a noisy image to match features extracted from a clean image), and feature complete (e.g., by requiring that features extracted from a noisy image be sufficient to generate a clean image). Accordingly, text recognition systems generalize to text not included in training images, and are robust to nuisance factors. Furthermore, since clean images are provided as supervision at feature and pixel levels, training requires fewer training images than text recognition systems that are not trained with a supervisory clean image, thus saving time and resources.

RELATED APPLICATION

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 16/184,779 filed Nov. 8, 2018 entitled “TrainingText Recognition Systems,” the disclosure of which is incorporated byreference herein in its entirety.

BACKGROUND

Text recognition systems recognize text in images, such as an image of anatural scene including text (e.g., an image of a highway sign), and areused in a variety of applications, including autonomous driving, robots(e.g., autonomous parking lot attendants), drones, aidingvisually-impaired persons, keyword parsing of a document, advertisementrecommendation for mobile clients, and the like. Because images withtext can be synthetically generated (e.g., using an image renderer thatintroduces nuisance factors to perturb text of an image), textrecognition systems are often trained with training datasets thatcontain large numbers of synthetically-generated text images, such as bycomparing text predicted by the text recognition system with groundtruth text supplied to the text recognition system.

The performance of text recognition systems, however, is usually limitedto recognizing those words included in training images used to train thetext recognition system, so that words not included in a trainingdataset used to train the text recognition system may not be properlyrecognized by the text recognition system. Consequently, the number ofimages in a training dataset is usually very large, and may includemultiple images with different nuisance factors for each word in alanguage. For instance, a training dataset may require hundreds ofthousands of images to train a convolutional neural network of a textrecognition system with each training class corresponding to a word ofthe English language. Accordingly, training a text recognition systemrequires significant effort, in terms of manual resources to design andselect a training dataset, and computer resources to process the imagesof the training dataset to train the text recognition system, requiringa significant amount of time.

Moreover, text recognition systems are often not robust to some nuisancefactors, such as the introduction of compression artifacts, additivenoise processes, geometric distortion (e.g., warping or deformation oftext), and the like. Adding training images to a training database toinclude additional nuisance factors, like different geometricdistortions, increases the time and effort needed to train a textrecognition system by increasing the resources needed to manage andprocess a training dataset.

SUMMARY

Techniques, systems, and devices are described to train a textrecognition system to recognize text in images. Text recognition systemsare trained not only with synthetic images of a training dataset thatinclude text perturbed by nuisance factors (e.g., noisy images), butalso with clean images that include clean text. For instance, a cleanimage may correspond to a synthetic image of a training dataset, buthave some or all nuisance factors removed, such as by removing aperspective warp introduced in a training image. A clean image acts assupervision at both feature and pixel levels, so that a text recognitionsystem is trained to be feature invariant (e.g., by requiring featuresextracted from a noisy image to match features extracted from a cleanimage), and feature complete (e.g., by requiring that features extractedfrom a noisy image be sufficient to generate a clean image). At thefeature level, a feature discriminator is adversarially trained againsta feature encoder using features extracted from a noisy image andfeatures extracted from a clean image by the feature encoder. At thepixel level, an image discriminator is adversarially trained against afeature encoder and image generator using the clean image and areconstructed clean image generated with the image generator fromfeatures extracted from the noisy image by the feature extractor. Theimage discriminator generates a confidence score that indicates aquality of text prediction across a dimension (e.g., horizontally) ofthe prediction of the text, and can be used to detect and correct errorsin a prediction of text. Accordingly, a text recognition system trainedby the techniques, systems, and devices described herein is not limitedto recognizing text in an image that corresponds to text of a trainingimage, but can generalize to text not included in training images usedto train the text recognition system, since the text recognition systemis trained to be feature complete. Moreover, a text recognition systemtrained by the techniques, systems, and devices described herein isrobust to nuisance factors, since the text recognition system is trainedto be feature invariant. Furthermore, since a clean image is provided assupervision at feature and pixel levels using adversarially-traineddiscriminators, a text recognition system trained by the techniques,systems, and devices described herein can be trained using fewertraining images than text recognition systems that are trained without asupervisory clean image, thus saving time and resources.

This Summary introduces a selection of concepts in a simplified formthat are further described below in the Detailed Description. As such,this Summary is not intended to identify essential features of theclaimed subject matter, nor is it intended to be used as an aid indetermining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different instances in thedescription and the figures may indicate similar or identical items.Entities represented in the figures may be indicative of one or moreentities and thus reference may be made interchangeably to single orplural forms of the entities in the discussion.

FIG. 1 illustrates a digital medium environment in an exampleimplementation that is operable to employ techniques described herein.

FIG. 2 illustrates example images in accordance with one or more aspectsof the disclosure.

FIG. 3 illustrates example images and example recognition results inaccordance with one or more aspects of the disclosure.

FIG. 4 illustrates an example system in accordance with one or moreaspects of the disclosure.

FIG. 5 illustrates a flow diagram depicting an example procedure inaccordance with one or more aspects of the disclosure.

FIG. 6 illustrates a flow diagram depicting an example procedure inaccordance with one or more aspects of the disclosure.

FIG. 7 illustrates a flow diagram depicting an example procedure inaccordance with one or more aspects of the disclosure.

FIG. 8 illustrates an example system including various components of anexample device that can be implemented as any type of computing deviceas described and/or utilized with reference to FIGS. 1-7 to implementaspects of the techniques described herein.

DETAILED DESCRIPTION

Overview

Text recognition systems can be used in many different situations torecognize text in an image, such as autonomous driving (e.g., byrecognizing street signs and guiding an automobile), robots (e.g.,autonomous parking lot attendants), drones, aiding visually-impairedpersons, keyword parsing of a document, advertisement recommendation formobile clients, and the like. Text recognition systems are often trainedwith training datasets that contain large numbers ofsynthetically-generated text images, e.g., noisy images including textperturbed by one or more nuisance factors, such as compressionartifacts, additive noise processes, geometric distortion, and the like.For instance, a training dataset may include hundreds of thousands ofnoisy images to train a convolutional neural network of a textrecognition system with each training class corresponding to a word ofthe English language. Hence, training a text recognition system canrequire significant resources, such as manual resources to design andselect a training dataset, and computer resources to process the imagesof the training dataset to train the text recognition system. Moreover,text recognition systems are often not robust to some nuisance factors,such as compression artifacts and geometric deformation of text, andadding additional training images to a training dataset for thesenuisance factors exacerbates the amount of resources needed when usinglarge training datasets.

Accordingly, this disclosure describes systems, techniques, and devicesfor training a text recognition system not only with noisy images of atraining dataset that include text perturbed by nuisance factors, butalso with supervisory clean images that include clean text, therebyreducing the number of images needed to train a text recognition systemand making the text recognition robust to nuisance factors. A cleanimage may correspond to a synthetic image of a training dataset, buthave some or all nuisance factors removed, such as by removing aperspective warp introduced in a synthetically-generated training image.A clean image acts as supervision at both feature and pixel levels, sothat a text recognition system is trained to be feature invariant andfeature complete.

A text recognition system is trained to be feature invariant byrequiring features extracted from a training image (e.g., a noisy image)with a feature encoder to match features extracted from a clean imagewith the feature encoder. The feature encoder can extract any suitablefeatures from an image, such as a feature map. A feature discriminatoris adversarially trained against the feature encoder using featuresextracted from the training image and features extracted from the cleanimage. For instance, the feature discriminator outputs a respectivebinary feature label for each feature map provided to it as input,attempting to distinguish between noisy inputs (e.g., features extractedfrom the training image) and clean inputs (e.g., features extracted fromthe clean image). The feature encoder and feature discriminator aretrained adversarially in a minimax style using a feature adversarialloss term determined from respective binary labels for featuresextracted from the training image and features extracted from the cleanimage.

A text recognition system is trained to be feature complete by requiringthat all text label information is extracted by the feature encoder froman image. This requirement is equivalent to requiring the existence ofan image generator that can generate a clean image from featuresextracted from a noisy image. Hence, an image generator receivesfeatures extracted from a training image using a feature encoder, andgenerates a reconstructed clean image. At the pixel level, an imagediscriminator is adversarially trained against the feature encoder andthe image generator using the clean image and the reconstructed cleanimage. For instance, the image discriminator outputs a respective binaryimage label for each image provided to it as input, attempting todistinguish between a reconstructed clean image generated by the imagegenerator and a clean image rendered from a ground truth text string.The feature encoder and image generator are trained adversariallyagainst the image discriminator in a minimax style using an imageadversarial loss term determined from respective binary labels for aclean image and reconstructed clean image.

In one example, an image discriminator generates a confidence score fromthe image adversarial loss term that indicates a quality of textprediction across a dimension of a prediction of text. For instance, atext prediction module may predict text of an image from featuresextracted from an image by a feature encoder, and the imagediscriminator may generate a confidence score between zero and oneacross a horizontal dimension of the text prediction. Hence, each letterof a text prediction may be assigned one or more confidence scoresacross the width of the letter, which can be used to detect and correcterrors. In one example, a text recognition system includes apost-processing step that receives a text prediction generated by thetext recognition system, and detects, corrects, or detects and correctserrors based on a confidence score generated from an image adversarialloss term.

Additionally or alternatively, a text recognition system can be trainedwith a loss term constructed from a weighted sum of loss terms,including a feature adversarial loss term as discussed above, an imageadversarial loss term as discussed above, a feature-matching loss termdetermined from a difference of a first feature map (e.g., a feature mapcorresponding to a noisy image) and a second feature map (e.g., afeature map corresponding to a clean image), an image-reconstructionloss term determined from a difference of a reconstructed clean imageand a clean image, and a training loss term determined from aconditional probability of a ground truth text string given a predictionof text based on features extracted from an image.

Accordingly, a text recognition system trained by the techniques,systems, and devices described herein is robust to nuisance factors andis not limited to recognizing text in an image that corresponds to textof a training image, but can generalize to text not included in trainingimages used to train the text recognition system, since the textrecognition system is trained to be feature invariant and featurecomplete. Furthermore, since a clean image is provided as supervision atfeature and pixel levels, a text recognition system trained by thetechniques, systems, and devices described herein can be trained usingfewer training images than text recognition systems that are not trainedwith a supervisory clean image, thus saving time and resources.

In the following discussion an example digital medium environment isdescribed that may employ the techniques described herein. Exampleimplementation details and procedures are then described which may beperformed in the example digital medium environment as well as otherenvironments. Consequently, performance of the example procedures is notlimited to the example environment and the example environment is notlimited to performance of the example procedures.

Example Digital Medium Environment

FIG. 1 is an illustration of a digital medium environment 100 in anexample implementation that is operable to employ techniques describedherein. As used herein, the term “digital medium environment” refers tothe various computing devices and resources that can be utilized toimplement the techniques described herein. The illustrated digitalmedium environment 100 includes a user 102 having at least one computingdevice. In the example in FIG. 1 , user 102 is illustrated as having twocomputing devices, computing device 104-1 and computing device 104-2(collectively computing devices 104). For instance, computing device104-1 depicts a smart phone, and computing device 104-2 depicts avehicle to indicate that computing device 104-2 is a vehicular computingdevice, such as an autonomous driving system of a vehicle. Computingdevices 104 are example computing devices, and any suitable computingdevice is contemplated, such as a mobile phone, tablet, laptop computer,desktop computer, gaming device, goggles, glasses, camera, digitalassistant, wearable device (e.g., watch, arm-band, adhesive patch,etc.), echo device, image editor, non-linear editor, digital audioworkstation, copier, scanner, vehicle, drone, and the like that mayinclude an application to recognize text in images. Furthermore,discussion of one of computing devices 104 is not limited to thatcomputing device, but generally applies to each of the computing devices104. Moreover, computing devices 104 may range from full resourcedevices with substantial memory and processor resources (e.g., personalcomputers, game consoles) to a low-resource device with limited memoryor processing resources (e.g., mobile devices).

Various types of input devices and input instrumentalities can be usedto provide input to computing devices 104. For example, computingdevices 104 can recognize input as being a mouse input, stylus input,touch input, input provided through a natural user interface, usergestures on a touchscreen, combinations thereof, and the like. Thus,computing devices 104 can recognize multiple types of gestures includingtouch gestures and gestures provided through a natural user interface.In one example, computing devices 104 include speech recognition,identification, and synthesis functionalities, microphones, and speakersthat allow computing devices 104 to communicate with user 102 in aconversation. Moreover, computing devices 104 can include an imagecapture device (e.g., a camera) configured to capture images and videostreams.

Furthermore, computing devices 104 may be representative of one or aplurality of different devices, such as one or more devices connected toa network that perform operations “over the cloud” as further describedin relation to FIG. 8 . In one example, computing devices 104 arecommunicatively coupled to each other, such as with a low power wirelesscommunication standard (e.g., a Bluetooth® protocol). For instance,computing device 104-1 can communicate wirelessly with computing device104-2. Hence, results of an image processed on one device (e.g.,computing device 104-1) can be communicated to, and displayed on anotherdevice (e.g., computing device 104-2).

Computing devices 104 are also coupled to network 106. Network 106communicatively couples computing devices 104 with server 108. Forclarity, only computing device 104-1 is illustrated in FIG. 1 as coupledto network 106, though computing device 104-2 can also be coupled toserver 108 via network 106. Network 106 may include a variety ofnetworks, such as the Internet, an intranet, local area network (LAN),wide area network (WAN), personal area network (PAN), cellular networks,terrestrial networks, satellite networks, combinations of networks, andthe like, and as such may be wired, wireless, or a combination thereof.

Server 108 may include one or more servers or service providers thatprovide services, resources, or combinations thereof to computingdevices 104. In one example, resources provided by server 108 may belicensed, purchased, or may be made freely available, (e.g., withoutauthentication, license, or account-based access). The resources caninclude any suitable combination of services and content, such as madeavailable over network 106 by one or more providers. Some examples ofservices include, but are not limited to, an on-line shopping service, aphoto editing service, an image database service (e.g., a serviceproviding training images from a database), a pre-trained model service(e.g., a service providing text recognition models that have beenpre-trained to recognize text in images), a web development andmanagement service, a collaboration service, a social networkingservice, a messaging service, an advertisement service, a graphicsdesign service, an image storage service (including storage and accessof photos, documents, records, files, and the like), and so forth.Content may include various combinations of assets, including videos,ads, audio, multi-media streams, animations, images, training images,web documents, web pages, applications, device applications, textdocuments, drawings, presentations, stock photographs, user profiles,user preferences, user data (e.g., images stored in an image gallery),and the like.

In the example in FIG. 1 , server 108 includes text recognition system110, which includes text recognition application 112 (discussed below inmore detail). Text recognition system 110 also includes image 114 andimage 116. Image 114 is an example of a noisy image, such as a trainingimage that is synthetically generated by rendering image 114 withnuisance factors based on a ground truth text string. For instance, aground truth text string for image 114 and image 116 may include thetext “ACM”, and image 114 can be rendered by applying any suitablerendering parameters to an image renderer provided the ground truth textstring. Image 114 may be rendered with rendering parameters that includea font type, text style (e.g., bold, italics, etc.), font size, etc.,and any suitable nuisance factors that perturb the text rendered inimage 114, such as rotation, background shadows, sensor noise,compression artifacts, additive noise processes, geometric distortion(e.g., warping or deformation of text), combinations thereof, and thelike.

Image 116 is an example of a clean image. Image 116 can be generated inany suitable way, such as by rendering image 116 based on a ground truthtext string according to rendering parameters used to render image 114.In one example, image 116 is rendered with the same rendering parameterssuch as font type, text style, and font size as image 114, withoutnuisance factors applied to image 116. Additionally or alternatively,image 116 can be rendered by removing one or more nuisance factorsapplied to image 114.

Image 114 and image 116 are examples of images used to train textrecognition system 110 to recognize text in images. For instance, image114 is a noisy image and image 116 is a clean image that serves assupervision to train text recognition system 110. Parts of textrecognition system 110 that have been pre-trained by server 108 usingimage 114 and image 116, such as a feature encoder and text decoder oftext recognition system 110, can be provided to one of computing devices104 via network 106 to recognize text in images. Accordingly, computingdevices 104 include text system 118. For clarity, only computing device104-2 is illustrated as including text system 118, though computingdevice 104-1 also includes a copy of text system 118.

In the example in FIG. 1 , text system 118 includes a pre-trained textrecognition system obtained from server 108 (e.g., a feature encoder andtext decoder). Computing device 104-2 obtains image 120, indicated by astreet sign in FIG. 1 . In one example, computing device 104-2 includesan imaging device, such as a camera, that obtains image 120. Image 120may be a stand-alone image, or an image in a video sequence of images.Using a pre-trained text recognition system of text system 118 providedby server 108, computing device 104-2 recognizes text included in image120 and generates a text prediction 122. Text prediction 122 includesthe text “NO EXIT”, corresponding to the text of image 120. Computingdevice 104-2 may use text prediction 122 to autonomously control thedriving of a vehicle that includes computing device 104-2, such as tooperate the vehicle without input of user 102. For instance, computingdevice 104-2 may determine that based on image 120 and text prediction122, the vehicle is prevented from exiting at an approaching exit whilethe vehicle is driving, and alert user 102 in any suitable way. In oneexample, computing device 104-2 communicates text prediction 122 tocomputing device 104-1, such as with a low-energy communication link,and computing device 104-1 displays text prediction 122 in a userinterface to alert user 102. In one example, computing device 104-2displays text prediction 122 in a heads-up-display of a vehicle.Additionally or alternatively, computing device 104-2 may adjust a speedof the vehicle, a position of the vehicle (e.g., change lanes on afreeway), and the like. In one example, computing device 104-2synthesizes an audio file including speech of text prediction 122, andplays back the audio file via a speaker, thereby alerting user 102 tothe text of image 120.

Text recognition system 110 includes display 124. Display 124 can be anysuitable type of display, such as a liquid crystal display, plasmadisplay, head-mounted display, projector and screen, a touchscreen thatrecognizes user gestures (e.g., touch gestures), and the like. Atouchscreen of display 124 can include any suitable type of touchscreen,such as a capacitive touchscreen, a resistive touchscreen, a surfaceacoustic wave touchscreen, an infrared touchscreen, an optical imagingtouchscreen, an acoustic pulse recognition touchscreen, combinationsthereof, and the like. Moreover, display 124 can display any suitableinterface.

Text recognition system 110 also includes processors 126. Processors 126can include any suitable type and number of processors. Hence, textrecognition system 110 may be implemented at least partially byexecuting instructions stored on storage 128 on processors 126. Forinstance, processors 126 may execute portions of text recognitionapplication 112.

Storage 128 can be any suitable type of storage accessible by orcontained in text recognition system 110. Storage 128 stores andprovides access to and from memory included in storage 128 for anysuitable type of data. For instance, storage 128 includes noisy imagedata 130, including data associated with noisy images, such as trainingimages, rendering parameters, nuisance factors applied to an image, anorder nuisance factors are applied, inverse parameters for nuisancefactors that can be applied to remove a respective nuisance factor, animage size, a ground truth text string, a format of an image (e.g., afile format), an image identifier in a sequence of images, such as atraining dataset or video sequence, thumbnail images of training images,combinations thereof, and the like.

Storage 128 also includes clean image data 132, including dataassociated with clean images, such as a ground truth text string,rendering parameters (e.g., font style and size, italics, bold, etc.),nuisance factors removed from a noisy image, an order nuisance factorsare removed, inverse parameters of nuisance factors removed from a noisyimage, nuisance factors applied to a clean image, an order nuisancefactors are applied to a clean image, an image size, a format of animage (e.g., a file format), an image identifier, combinations thereof,and the like.

Storage 128 also includes reconstructed image data 134, including dataassociated with reconstructed clean images, such as reconstructed cleanimages generated by an image generator of text recognition system 110,parameters of an image generator used to generate a reconstructed cleanimage (e.g., a state, values of weights, architecture configuration,etc. of an image generator), features used to generate a reconstructedclean image (e.g., features extracted from a noisy image), a value of animage-reconstruction loss term determined from the difference of areconstructed clean image and a clean image, combinations thereof, andthe like.

Storage 128 also includes feature data 136, including data associatedwith features of images, such as features extracted from noisy images,features extracted from clean images, feature maps, frames of flattenedfeature maps, probability distributions of frames of feature maps,parameters of a feature encoder used to extract features (e.g., a state,values of weights, architecture configuration, etc. of a featureencoder), a value of a feature-matching loss term determined from thedifference of a first feature map (e.g., a feature map extracted from anoisy image) and a second feature map (e.g., a feature map extractedfrom a clean image), combinations thereof, and the like.

Storage 128 also includes discriminator data 138, including dataassociated with image discriminators and feature discriminators, such asbinary feature labels that indicate whether features input to a featurediscriminator are noisy features (e.g., features extracted from a noisyimage) or clean features (e.g., features extracted from a clean image),binary image labels that indicate whether images input to an imagediscriminator are reconstructed clean images or clean images, a value ofa feature adversarial loss term determined from respective binary labelsfor a first feature map (e.g., a feature map extracted from a noisyimage) and a second feature map (e.g., a feature map extracted from aclean image), a value of an image adversarial loss term determined fromrespective binary labels for a reconstructed clean image and a cleanimage, a confidence score determined from an image adversarial lossterm, combinations thereof, and the like.

Storage 128 also includes text data 140, including data associated withtext of images, such as a ground truth text string, a text predictiongenerated by a text decoder of text recognition system 110, a confidencescore that indicates a quality of a prediction of text across adimension of the prediction of the text, such as horizontally, a valueof a training loss term determined from a conditional probability of theground truth text string given the prediction of the text, characters oftext determined to be in error, characters of text corrected by textrecognition system 110, combinations thereof, and the like.

Storage 128 also includes training data 142, including data associatedwith text of images, such as training losses computed while training atext recognition system (e.g., a feature-matching loss term, a featureadversarial loss term, an image-reconstruction loss term, an imageadversarial loss term, and a training loss term), combining weights ofloss terms, indicators of images of a training dataset used to train atext recognition system, rendering parameters used to synthesize noisyimages, nuisance factors applied to an image to perturb text of animage, combinations thereof, and the like.

Furthermore, text recognition system 110 includes transceiver module144. Transceiver module 144 is representative of functionalityconfigured to transmit and receive data using any suitable type andnumber of communication protocols. For instance, data within textrecognition system 110 may be transmitted from server 108 withtransceiver module 144, such as from server 108 to one of computingdevices 104. Furthermore, data can be received at server 108 withtransceiver module 144. In one example, transceiver module 144 includesa low power wireless communication standard (e.g., a Bluetooth®protocol) for communicating data between computing devices.

Text recognition system 110 also includes assets 146. In one example,assets 146 are stored in storage 128. Assets 146 can include anysuitable asset used or generated by text recognition system 110. In oneexample, assets 146 include parts of text recognition systems that havebeen trained to recognize text in images, such as a feature encoder andtext decoder. Hence, pre-trained feature encoders and text decoders ofassets 146 can be provided from server 108 to computing devices 104 vianetwork 106 and used in any suitable application to recognize text inimages, such as image 120.

Text recognition system 110 also includes text recognition application112. Text recognition application 112 includes noisy image module 148,clean image module 150, feature extraction module 152, text predictionmodule 154, image generator module 156, feature discriminator module158, image discriminator module 160, and training module 162. Thesemodules work in conjunction with each other to train text recognitionsystems to recognize text in images, such as a feature encoder offeature extraction module 152 and a text decoder of text predictionmodule 154.

Noisy image module 148 is representative of functionality configured toobtain a noisy image, such as a noisy image including text based on aground truth text string. Noisy image module 148 can obtain any suitablenoisy image in any suitable way. In one example, noisy image module 148synthesizes a noisy image to include text of the ground truth textstring according to rendering parameters that include canonicalparameters and nuisance factors. Canonical parameters describe how thetext is to be rendered in the absence of nuisance factors, such as fonttype, text style (e.g., italics, bold, superscript, subscript, etc.),font size, and the like. Nuisance factors describe perturbations thatcan be applied to an image, such as shading, color variations,perspective warping, geometric deformations, additive noise (e.g.,sensor noise), compression artifacts (e.g., pixelization), and the like.In one example, noisy image module 148 synthesizes noisy images byrandomly sampling canonical parameters and nuisance factors, such asuniformly, and applying the randomly-sampled canonical parameters andnuisance factors to synthesize the noisy image.

Additionally or alternatively, noisy image module 148 can obtain noisyimages from a database of images, such as a database including atraining dataset of training images that include text. In one example,training data 142 includes a database of training datasets of imagesthat include text. Images of a training dataset may include metadataincluding rendering parameters, such as canonical parameters andnuisance factors used to synthesize the images, an order nuisancefactors were applied to a noisy image, a size of an image (e.g., aresolution in number of pixels), a date an image was generated, a formatof an image (e.g., a file format), an image identifier in a sequence ofimages, such as a video sequence or a training dataset, a thumbnailversion of an image, and the like.

In one example, noisy image module 148 resizes a noisy image. Noisyimage module 148 can resize a noisy image in any suitable way, such asby cropping the image so that the noisy image includes substantiallyonly text, and removing background portions that do not include text.For instance, noisy image module 148 may draw a bounding box around textof an image, and crop the image to include only the bounding box.

Noisy images obtained by noisy image module 148, along with any suitableinformation, such as a source location of an image, a file format of animage, an indication whether the image is related to other images, suchas a sequence number in a training dataset or video sequence, imagemetadata, rendering parameters, nuisance factors, a thumbnail version ofan image, a ground truth text string, and the like, used by orcalculated by noisy image module 148 are stored in noisy image data 130of storage 128 and made available to modules of text recognitionapplication 112. In one example, noisy image module 148 synthesizes anoisy image according to rendering parameters and a ground truth textstring, and provides the noisy image to feature extraction module 152.

Clean image module 150 is representative of functionality configured todetermine a clean image including text. Clean image module 150 candetermine any suitable clean image in any suitable way. In one example,clean image module 150 determines a clean image by removing at least onenuisance factor from a noisy image to form the clean image. Forinstance, clean image module 150 may obtain a noisy image synthesized bynoisy image module 148, including metadata describing nuisance factorsused to generate the noisy image, and form a clean image from the noisyimage by removing one of the nuisance factors described in the metadataof the noisy image.

Additionally or alternatively, clean image module 150 may synthesize aclean image without applying one or more nuisance factors that wereapplied to a noisy image. For instance, clean image module 150 mayobtain a noisy image synthesized by noisy image module 148, includingmetadata describing canonical parameters and nuisance factors used togenerate the noisy image, and synthesize a clean image according to thecanonical parameters, with one or more of the nuisance factors zeroed.In one example, clean image module 150 obtains a clean image bysynthesizing the clean image without nuisance factors (e.g., allnuisance factors are zeroed) based on a ground truth text string used bynoisy image module 148 to synthesize a noisy image. Clean image module150 may synthesize the clean image with the same canonical parametersused by noisy image module 148 to synthesize the noisy image. Hence,text of a clean image generated by clean image module 150 may be in asame font, size, style, etc. as text of a noisy image generated by noisyimage module 148. An example of a noisy image generated by noisy imagemodule 148 and various clean images generated by clean image module 150that correspond to the noisy image are illustrated in to FIG. 2 .

FIG. 2 illustrates example images 200 in accordance with one or moreaspects of the disclosure. Images 200 includes noisy image 202 and threeclean images corresponding to noisy image 202, including clean image204, clean image 206, and clean image 208. Noisy image 202 is an exampleof a noisy image obtained by noisy image module 148. For instance, noisyimage 202 is synthesized to include the ground truth text string“IMPRESSES” according to multiple nuisance factors, including backgroundshadow, rotation, compression, and additive noise.

Clean image 204, clean image 206, and clean image 208 are examples ofclean images generated by clean image module 150 that correspond tonoisy image 202. For instance, clean image 204 is a binarized version ofnoisy image 202 in which the nuisance factor of the background shadowhas been removed, and clean image 206 is a deskewed version of noisyimage 202 in which the nuisance factor of the rotation has been removed.Clean image 208 is synthesized based on the ground truth text stringwithout nuisance factors, so that the text of clean image 208 is notperturbed by nuisance factors.

Noisy image 202, together with any one of clean image 204, clean image206, or clean image 208, can be used as a training pair of images totrain a text recognition system, such as text recognition system 110 inFIG. 1 . A clean image serves as supervision at the feature level andpixel level. Relative performance in terms of recognition accuracy of atext recognition system trained with image pairs including a noisy imageand a clean image is illustrated below in Table 1 for a dataset ofimages.

TABLE 1 Comparison of Recognition Results for Different Clean ImagesClean Image Recognition Accuracy (%) None 80.8 Binarized 85.8 Deskewed84.7 No Nuisance Factors 87.0

Table 1 illustrates that when no clean image is used for trainingsupervision, a recognition accuracy of 80.8 percent is achieved.However, when a binary version of a noisy image is obtained by removinga background shadow (as in clean image 204) and used as a supervisingclean image, recognition accuracy improves by five percent to 85.8percent. When a deskewed version of a noisy image is obtained byremoving a rotation (as in clean image 206) and used as a supervisingclean image, recognition accuracy improves to 84.7 percent. However,when a clean image is synthesized without nuisance factors (as in cleanimage 208) and used as a supervising clean image, recognition accuracyimproves significantly to 87.0 percent. Accordingly, the addition of aclean image to a noisy image as a supervisory input to controlfeature-level and pixel-level matching during training improves therecognition accuracy of the text recognition system.

Returning to FIG. 1 , clean images obtained by clean image module 150,along with any suitable information, such as a source location of animage, a file format of an image, an indication whether the image isrelated to other images, such as a sequence number in a training datasetor video sequence of images, image metadata, rendering parameters,canonical parameters, nuisance factors, a thumbnail version of an image,and the like, used by or calculated by clean image module 150 are storedin clean image data 132 of storage 128 and made available to modules oftext recognition application 112. In one example, clean image module 150synthesizes a clean image according to rendering parameters and a groundtruth text string, and provides the clean image to feature extractionmodule 152 and image discriminator module 160.

Feature extraction module 152 is representative of functionalityconfigured to extract features from images. In one example, featureextraction module 152 extracts features of a noisy image obtained bynoisy image module 148 into a first feature map, and extracts featuresof a clean image obtained by clean image module 150 into a secondfeature map.

Feature extraction module 152 can extract features from an image in anysuitable way. In one example, feature extraction module 152 includes afeature encoder, E, that extracts a feature map from an image. Featureencoder, E, can include any suitable encoder, such as a fullyconvolutional neural network that extracts a three-dimensional (3D)feature map from an input image provided to it. Earlier layers of theconvolutional neural network may extract low-level features, such asedges and corners, and later layers of the convolutional neural networkmay extract finer-level features, such as texture.

A feature map generated by feature extraction module 152 may be providedto text prediction module 154. In one example, feature extraction module152 extracts a 3D feature map f from an image, such as a noisy imageobtained by noisy image module 148, and transforms the 3D feature mapinto a sequence of feature frames {f¹, f², . . . f^(N)} by flattening Nfeature segments sliced from f horizontally (e.g., from left to right).Each feature frame f^(n), n=1 . . . N, corresponds to a local region ofthe input image which may contain one or more parts of a text glyph.Feature extraction module 152 may provide the sequence of feature framesfor a feature map to text prediction module 154, which predicts text ofthe input image from the sequence of feature frames through beam search(discussed below in more detail).

A feature encoder, E, of feature extraction module 152 can be trainedwith any suitable loss term. In one example, a feature encoder, E, istrained to minimize a feature-matching loss term determined from adifference of a first feature map extracted from a noisy image (e.g., anoisy image obtained by noisy image module 148) and a second feature mapextracted from a clean image (e.g., a clean image obtained by cleanimage module 150). For instance, a feature encoder, E, may be trainedaccording to a feature-matching loss term

_(f), or

${\min\limits_{E}\mathcal{L}_{f}} = {{{E(x)} - {E\left( \overset{¯}{x} \right)}}}_{2}$where x denotes a noisy image and x denotes a clean image, and E(⋅)denotes a feature map extracted from an image with feature encoder E.Since the noisy image and the clean image share a same text label (e.g.,they may both be synthesized from a same ground truth text string), thefeature-matching loss term serves to train the feature encoder, E, to befeature invariant.

Features extracted by feature extraction module 152, along with anysuitable information, such as a feature map, a sequence of featureframes, a value of a feature-matching loss term, parameters of a featureencoder used to extract features (e.g., a state, values of weights,architecture configuration, etc. of a feature encoder), and the like,used by or calculated by feature extraction module 152 are stored infeature data 136 of storage 128 and made available to modules of textrecognition application 112. In one example, feature extraction module152 extracts features of a noisy image into a first feature map,extracts features of a clean image into a second feature map, andprovides the first feature map to feature discriminator module 158, textprediction module 154, and image generator module 156, and the secondfeature map to feature discriminator module 158.

Text prediction module 154 is representative of functionality configuredto generate a prediction of text. Text prediction module 154 cangenerate a prediction of text in any suitable way. In one example, textprediction module 154 includes a text decoder, T, that generates aprediction of text based on a feature map provided by feature extractionmodule 152. A feature encoder, E, of feature extraction module 152 and atext decoder, T, of text prediction module 154 form an encoder-decoderstructure representing a deep neural network.

A text decoder, T, of text prediction module 154 can be any suitabletext decoder. In one example, a text decoder, T, of text predictionmodule 154 includes a two-layer bidirectional long-short term memory(BLSTM) network that predicts text by solving a sequence labelingproblem. The text decoder, T, may receive a sequence of feature frames{f¹, f², . . . f^(N)} of a feature map from feature extraction module152, as discussed above. Text decoder, T, can predict a characterprobability distribution π^(n), n=1 . . . N, based on the dependencyamong the feature frames. In one example, the probability space of π^(n)includes all English alphanumeric characters and a blank token for wordseparation. Text decoder, T, can translate the sequence of per-framecharacter probability distributions {π¹, π², . . . π^(N)} into aprediction of text of an image, ŷ, in any suitable way, such as throughbeam search.

A text decoder, T, of text prediction module 154 can be trainedaccording to any suitable loss term. In one example, a text decoder, T,of text prediction module 154 and a feature encoder, E, of featureextraction module 152 are jointly trained according to a training lossterm determined from a conditional probability of a ground truth textstring given the prediction of text. For instance, a text decoder, T,and a feature encoder, E, can be jointly trained by minimizing thediscrepancy between the probability sequence {π¹, π², . . . π^(N)} and aground truth text string, y, using a connectionist temporalclassification technique (CTC) as described in “Connectionist temporalclassification: labelling unsegmented sequence data with recurrentneural networks”, Proceedings of the 23^(rd) international conference onmachine learning, ACM, pp. 369-376, 2006, by A. Graves et al., thedisclosure of which is incorporated herein by reference in its entirety.A CTC technique aligns the variable-length character sequence of y withthe fixed-length probability sequence so that the conditionalprobability of y given ŷ can be evaluated based on the probabilitysequence {π¹, π², . . . π^(N)}. Accordingly, a text decoder, T, and afeature encoder, E, can be jointly trained according to a training lossterm

_(y), or

${\min\limits_{E,T}\mathcal{L}_{y}} = {{P\left( y \middle| \overset{\hat{}}{y} \right)} = {\sum\limits_{{\overset{˜}{y}:{\beta(\overset{\sim}{y})}} = y}{\prod\limits_{t = 1}^{K}{\pi^{t} \cdot {\overset{˜}{y}}^{t}}}}}$where ŷ=T(E(x)) is a prediction of text of an image from text decoder T,P(⋅) denotes probability, β is a CTC mapping for sequences of length K,and {tilde over (y)}^(t) denotes a t-th token in {tilde over (y)}.

In one example, a text decoder, T, of text prediction module 154 and afeature encoder, E, of feature extraction module 152 that have beenpre-trained are provided to one of computing devices 104 to recognizetext in images. For instance, text system 118 may receive a pre-trainedtext decoder, T, and a pre-trained feature encoder, E, from server 108as part of a text recognition system to recognize text in images, suchas to generate text prediction 122 from image 120.

A prediction of text generated by text prediction module 154, along withany suitable information, such as a feature map, a sequence of featureframes, a value of a feature-matching loss term, parameters of a featureencoder used to extract features (e.g., a state, values of weights,architecture configuration, etc. of a feature encoder), and the like,used by or calculated by text prediction module 154 are stored in textdata 140 of storage 128 and made available to modules of textrecognition application 112. In one example, text prediction module 154provides a prediction of text to training module 162. Additionally oralternatively, text prediction module 154 may expose a prediction oftext in a user interface.

Image generator module 156 is representative of functionality configuredto generate a reconstructed clean image from features extracted byfeature extraction module 152. Image generator module 156 can generate areconstructed clean image in any suitable way. In one example, imagegenerator module 156 includes an image generator, G, that generates areconstructed clean image, {circumflex over (x)}, from a feature mapextracted from a noisy image, x, obtained by noisy image module 148, sothat the reconstructed clean image can be expressed as {circumflex over(x)}=G(E(x)).

An image generator, G, of image generator module 156 can generate areconstructed clean image in any suitable way. In one example, an imagegenerator, G, of image generator module 156 includes a deconvolutionalneural network to generate a reconstructed clean image from a featuremap. For feature completeness, image generator, G, is trained togenerate a reconstructed clean image that matches a clean image obtainedby clean image module 150, ensuring that features extracted by featureextraction module 152 include all text information (e.g., the extractedfeatures are complete). Hence, an image generator, G, of image generatormodule 156 and a feature encoder, E, can be jointly trained according toan image-reconstruction loss term

_(g), determined from a difference between the reconstructed clean imageand the clean image, or

${\min\limits_{E,G}\mathcal{L}_{g}} = {{{G\left( {E(x)} \right)} - \overset{¯}{x}}}_{1}$where x is a noisy image obtained by noisy image module 148, x is aclean image obtained by clean image module 150, and {circumflex over(x)}=G(E(x)) is a reconstructed clean image generated by image generatormodule 156.

A reconstructed clean image generated by image generator module 156,along with any suitable information, such as a feature map used togenerate a reconstructed clean image, a value of an image-reconstructionloss term, parameters of an image generator used to generate areconstructed clean image (e.g., a state, values of weights,architecture configuration, etc. of an image generator), and the like,used by or calculated by image generator module 156 are stored inreconstructed image data 134 of storage 128 and made available tomodules of text recognition application 112. In one example imagegenerator module 156 provides a reconstructed clean image to imagediscriminator module 160.

Feature discriminator module 158 is representative of functionalityconfigured to distinguish between features extracted from a noisy imageand features extracted from a clean image. Feature discriminator module158 can distinguish between noisy and clean features (e.g., betweenfeatures extracted from a noisy image and features extracted from aclean image, respectively) using a feature discriminator, D_(F). Forinstance, a feature discriminator, D_(F), of feature discriminatormodule 158 can be trained adversarially against a feature encoder, E, offeature extraction module 152 in a generative adversarial manner, asdescribed in “Generative adversarial nets”, Advances in neuralinformation processing systems, pp. 2672-2680, 2014, by I. Goodfellow etal., the disclosure of which is incorporated herein by reference in itsentirety.

In one example, a feature discriminator, D_(F), of feature discriminatormodule 158 includes a convolutional neural network with binaryclassification outputs. For instance, feature discriminator module 158may receive a feature map extracted from an image by feature extractionmodule 152, and assign a feature binary label for the feature map toindicate whether the feature map was extracted from a noisy image or aclean image. Feature discriminator module 158 can assign any suitablefeature binary label, such as a first numerical value to indicate thatfeatures are extracted from a noisy image and a second numerical valueto indicate that features are extracted from a clean image.

A feature discriminator, D_(F), of feature discriminator module 158 canbe trained adversarially against a feature encoder, E, of featureextraction module 152 in a minimax style according to a featureadversarial loss term,

_(fa). In one example, a feature adversarial loss term is determinedfrom respective binary labels D_(F)(⋅) for a first feature map extractedfrom a noisy image and a second feature map extracted from a cleanimage, or

${\underset{E}{\min}\max\limits_{D_{F}}\mathcal{L}_{fa}} = {{\log\left\lbrack {D_{F}\left( {E\left( \overset{¯}{x} \right)} \right)} \right\rbrack} + {{\log\left\lbrack {1 - {D_{F}\left( {E(x)} \right)}} \right\rbrack}.}}$Accordingly, clean image x serves as supervision at the feature levelsince the feature discriminator, D_(F), is trained to distinguishbetween noisy and clean features.

Binary classification labels generated by feature discriminator module158 (e.g., feature binary labels), along with any suitable information,such as feature maps used to generate binary labels, a value of afeature adversarial loss term, parameters of a feature discriminatorused to generate classification labels (e.g., a state, values ofweights, architecture configuration, etc. of a feature discriminator),and the like, used by or calculated by feature discriminator module 158are stored in discriminator data 138 of storage 128 and made availableto modules of text recognition application 112. In one example, featurediscriminator module 158 provides respective binary labels generated fora first feature map extracted from a noisy image and a second featuremap extracted from a clean image to training module 162.

Image discriminator module 160 is representative of functionalityconfigured to distinguish between clean images and reconstructed cleanimages. Image discriminator module 160 can distinguish between cleanimages and reconstructed clean images using an image discriminator,D_(I). For instance, an image discriminator, D_(I), of imagediscriminator module 160 can be trained adversarially against a featureencoder, E, of feature extraction module 152 and an image generator, G,of image generator module 156 in a generative adversarial manner.

In one example, an image discriminator, D_(I), of image discriminatormodule 160 includes a convolutional neural network with binaryclassification outputs. For instance, image discriminator module 160 mayreceive an image, such as a clean image obtained by clean image module150 or a reconstructed clean image generated by image generator module156, and assign a binary label for the image to indicate whether theimage is a clean image or a reconstructed clean image (e.g. image binarylabels). Image discriminator module 160 can assign any suitable imagebinary label, such as a first numerical value to indicate that an imageis a clean image synthesized by clean image module 150 and a secondnumerical value to indicate an image is a reconstructed clean imagegenerated by image generator module 156.

An image discriminator, D_(I), of image discriminator module 160 can betrained adversarially against a feature encoder, E, of featureextraction module 152 and an image generator, G, of image generatormodule 156 in a minimax style according to an image adversarial lossterm,

_(ga). In one example, an image adversarial loss term is determined fromrespective binary labels D_(I)(⋅) for a reconstructed clean imageconditioned on a noisy image and a clean image conditioned on a noisyimage, or

${\underset{E,G}{\min}\underset{D_{I}}{\max}\mathcal{L}_{ga}} = {{\log\left\lbrack {D_{I}\left( \overset{¯}{x} \middle| x \right)} \right\rbrack} + {{\log\left\lbrack {1 - {D_{I}\left( {G\left( {E(x)} \right)} \middle| x \right)}} \right\rbrack}.}}$Accordingly, clean image x serves as supervision at the image level(e.g., at the pixel level) since the image discriminator, D_(I), istrained to distinguish between clean images and reconstructed cleanimages.

Image discriminator module 160 also generates a confidence score. Imagediscriminator module 160 can generate a confidence score in any suitableway. In one example, image discriminator module 160 generates aconfidence score from an image adversarial loss term

_(ga), such as by evaluating the image adversarial loss term at a numberof local image regions that may contain part of a text glyphhorizontally from left to right along a prediction of text. As anexample of a confidence score generated by image discriminator module160, consider FIG. 3 .

FIG. 3 illustrates example images and example recognition results 300 inaccordance with one or more aspects of the disclosure. Images andresults 300 include three rows representing three examples of inputimages, output images, and results generated by a text recognitionsystem, such as text recognition system 110 in FIG. 1 . Row 302illustrates an example for the text “MOSSER”, row 304 illustrates anexample for the text “COFFEE”, and row 306 illustrates an example forthe text “RESTAURANT”.

For each row, training inputs include noisy image x and clean image xthat can be used to train a text recognition system, such as textrecognition system 110 in FIG. 1 . Furthermore, a reconstructed cleanimage {circumflex over (x)} is shown for each row, generated by imagegenerator module 156 from features extracted by feature extractionmodule 152 from noisy image x. A confidence score generated by imagediscriminator module 160, and a prediction of text generated by textprediction module 154 are also illustrated for each row, together with aground truth text string.

For row 302 and row 306, the text recognition system correctly predictsthe respective text strings “MOSSER” and “RESTAURANT”. However, for row304, due to the extreme distortion of the noisy image, the textrecognition system is partly in error in forming a text prediction. Forinstance, reconstructed clean image 308 does not accurately reflect thetext “COFFEE” of the noisy input image, causing the text recognitionsystem to generate a text prediction “COFFLE”, rather than “COFFEE” at310. A confidence score for row 302 is shown at confidence score 312.

Confidence score 312 is a normalized score between zero and one andindicates a degree of confidence across a horizontal dimension of thetext prediction. A value of a confidence score close to one indicates ahigh confidence in the text prediction and that the reconstructed cleanimage looks realistic, while a value of a confidence score close to zeroindicates a low confidence in the text prediction and that thereconstructed clean image looks unrealistic. In the example in FIG. 3 ,an image adversarial loss term is evaluated at 25 positions across thetext prediction to determine 25 values of the confidence score, eachnormalized between zero and one. Confidence score 312 indicates thatconfidence is low for the last two letters of the text prediction, sinceconfidence score 312 decreases substantially in these regions. Hence,confidence score 312 matches reconstructed clean image 308, in which theground truth text is not properly rendered from the features extractedfrom the nosy input image.

Furthermore, for row 306, confidence score 314 indicates a lowconfidence in the text prediction, despite the text prediction matchingthe ground truth text string for “RESTAURANT”. Confidence score 314 islow throughout the central region of the text prediction because thereconstructed clean image for “RESTAURANT” is blurry for the middleletters between “R” and “NT”.

Accordingly, a confidence score generated by image discriminator module160 can indicate a reliability of a predicted text string. In oneexample, text recognition system 110 includes a post-processing step todetect and correct errors in the predicted text string based on theconfidence score. For instance, if a confidence score generated by imagediscriminator module 160 is below a threshold confidence, such as 0.1,for a threshold number of consecutive positions along a dimension of thepredicted text, such as six consecutive positions, text recognitionsystem 110 may declare that an error has been detected in the predictedtext. In one example, if text recognition system 110 detects an error inthe predicted text, text recognition system 110 corrects the error. Textrecognition system 110 can correct the error in any suitable way, suchas by reprocessing an input noisy image with different weights of aconvolutional network, changing a character of a text prediction (e.g.,based on other characters of a text prediction that have a highconfidence score, text recognition system 110 may determine a correctcharacter to replace an incorrect error), performing a spell checkalgorithm on the text prediction, combinations thereof, and the like.

Returning again to FIG. 1 , binary classification labels generated byimage discriminator module 160, along with any suitable information,such as images used to generate binary labels, a value of an imageadversarial loss term, confidence scores, parameters of an imagediscriminator used to generate classification labels (e.g., a state,values of weights, architecture configuration, etc. of an imagediscriminator), and the like, used by or calculated by imagediscriminator module 160 are stored in discriminator data 138 of storage128 and made available to modules of text recognition application 112.In one example, image discriminator module 160 provides respectivebinary labels generated for a clean image obtained by clean image module150 and a reconstructed clean image generated by image generator module156 to training module 162.

As discussed above, the modules of text recognition system 110 caninclude any suitable neural network or networks. Example parameters of afeature encoder, E, of feature extraction module 152, a text decoder, T,of text prediction module 154, an image generator, G, of image generatormodule 156, a feature discriminator, D_(F), of feature discriminatormodule 158, and an image discriminator, D_(I), of image discriminatormodule 160 for an example implementation of text recognition system 110are illustrated in Table 2.

TABLE 2 Example Parameters of Text Recognition System 110 Encoder ImageGenerator Layer Filter/Stride Output Size Layer Filter/Stride OutputSize Input — 32 × 100 × 3 Conv1 3 × 3/2 × 2 16 × 50 × 64 FConv7 2 × 2/2× 1  2 × 25 × 512  Conv2 3 × 3/2 × 2  8 × 25 × 128 FConv6 3 × 3/2 × 1  4× 25 × 512 Conv3 3 × 3/1 × 1  8 × 25 × 256  FConv5 3 × 3/1 × 1  4 × 26 ×256 Conv4 3 × 3/2 × 1  4 × 25 × 256 FComv4 3 × 3/2 × 1  8 × 25 × 256Conv5 3 × 3/1 × 1  4 × 28 × 512 FComv3 3 × 3/1 × 1  8 × 25 × 256 Conv6 3× 3/2 × 1  2 × 25 × 512 FConv2 3 × 3/2 × 2 16 × 50 × 128 Conv7 2 × 2/2 ×1  1 × 25 × 512 FConv1 3 × 3/2 × 2 32 × 100 × 3 Feature DiscriminatorImage Discriminator Layer Filter/Stride Output Size Layer Filter/StrideOutput Size ConvF1 1 × 1/1 × 1 1 × 25 × 256 ConvI1 3 × 3/2 × 2 16 × 80 x64 ConvF2 1 × 1/1 × 1 1 × 25 × 128 ConvI2 3 × 3/2 × 2  8 × 25 × 128ConvF3 1 × 1/1 × 1 1 × 25 × 64 ConvI3 3 × 3/2 × 1  4 × 25 × 256 ConvF4 1× 1/1× 1 1 × 25 × 32 ConvI4 3 × 3/2 × 1  2 × 25 × 256 ComvF5 1 × 1/1 × 11 × 28 × 1 ConvI5 2 × 2/2 × 1  1 × 28 × 1 AvgPool 1 × 25/1 × 1  1 × 1 ×1 AvgPool  1 × 25/1 × 1   1 × 1 × 1 Text Decoder Layer Hidden UnitOutput Size BLSTM1  256 25 × 512 BLSTM2 256 25 × 512 Output  37 25 × 37

Training module 162 is representative of functionality configured totrain a text recognition system to recognize image text (e.g., text inan image). Training module 162 can train a text recognition in anysuitable way. In one example, training module 162 trains modules of textrecognition system 110 based on a combination of loss terms describedabove, including a feature-matching loss term determined from thedifference of a first feature map and a second feature map, a featureadversarial loss term determined from respective binary labels for thefirst feature map and the second feature map, an image-reconstructionloss term determined from the difference of a reconstructed clean imageand a clean image, an image adversarial loss term determined fromadditional respective binary labels for the reconstructed clean imageand the clean image, and a training loss term determined from aconditional probability of a ground truth text string given a predictionof the text.

For instance, training module 162 can train modules of text recognitionsystem 110 according to an overall loss term,

, based on a combination of loss terms described above, or

$\begin{matrix}{\min\limits_{E,T,G}\max\limits_{D_{I},D_{F}}{\mathbb{E}}_{x,\overset{\_}{x},y}\left\{ {\mathcal{L}\left( {x,\overset{¯}{x},y} \right)} \right\}} \\{{\mathcal{L}\left( {x,\overset{¯}{x},y} \right)} = {{\lambda_{y}\mathcal{L}_{y}} + {\lambda_{f}\mathcal{L}_{f}} + {\lambda_{g}\mathcal{L}_{g}} + {\lambda_{ga}\mathcal{L}_{ga}} + {\lambda_{fa}\mathcal{L}_{fa}}}}\end{matrix}$where

_(x,x,y){⋅} denotes statistical expectation over training inputs made upof triples x, x, y, including a noisy image, a clean image, and a groundtruth text string, respectively, and the λ's are combining weights thatcan be assigned any suitable value, such as a number in a range ofnumbers (e.g., range [0,10]). In one example, λ_(y)=λ_(ga)=λ_(fa)=1,λ_(g)=10, and λ_(f)=0.001.

In one example, training module 162 pre-processes an image (e.g., anoisy image, a clean image, or both) before it is provided to textrecognition system 110 for training. In one example, training module 162resizes noisy images obtained by noisy image module 148 to a specifiedsize, such as 32×100 pixels in horizontal and vertical dimensions,respectively. Additionally or alternatively, training module 162 mayresize a clean image obtained by clean image module 150, such as to be asame size as a noisy image resized by training module 162. In oneexample, training module 162 scales image intensities to be within aprescribed range, such as between [−1,1].

Training module 162 can train a text recognition system in any suitableway, such as by generating training updates based on training data anddata generated by the text recognition system, and providing thetraining updates to the text recognition system to update the textrecognition system. Training updates may include any suitable updateterm, such as updates based on a stochastic gradient descent of a costsurface determined from a loss term, such as an overall loss term,

.

Accordingly, training module 162 can train a feature encoder, E, offeature extraction module 152, a text decoder, T, of text predictionmodule 154, an image generator, G, of image generator module 156, afeature discriminator, D_(F), of feature discriminator module 158, andan image discriminator, D_(I), of image discriminator module 160.Training module 162 can train these modules of a text recognition systemusing not only a noisy image and ground truth text string as traininginputs, but also a clean image that serves as supervision at bothfeature and image levels, to ensure that text recognition system is bothfeature invariant and feature complete.

Training updates generated by training module 162, along with anysuitable information, such as loss terms, confidence scores, combiningweights, iteration number, training updates, and the like, used by orcalculated by training module 162 are stored in training data 142 ofstorage 128 and made available to modules of text recognitionapplication 112. In one example, training module 162 provides trainingupdates to a feature encoder, E, of feature extraction module 152, atext decoder, T, of text prediction module 154, an image generator, G,of image generator module 156, a feature discriminator, D_(F), offeature discriminator module 158, and an image discriminator, D_(I), ofimage discriminator module 160.

In the example in FIG. 1 , text recognition system 110 trains a textrecognition system, such as a text recognition system including textrecognition application 112, and provides at least some modules of thetrained text recognition system to computing devices 104 for use in aclient application. In one example, server 108 provides a pre-trainedfeature encoder, E, of feature extraction module 152, and a pre-trainedtext decoder, T, of text prediction module 154 to at least one ofcomputing devices 104. Hence, each of computing devices 104 includestext system 118 that may receive and store a pre-trained textrecognition system.

Text system 118 includes applications 164, which can include anysuitable application, such as an application configured to be executedby one or more of computing devices 104. Applications 164 includesdetection application 166. Detection application 166 can be anyapplication configured to recognize text in an image, such as an imageediting application, a vehicular application (e.g., a guidance system ina self-driving car), a control system of a robot or drone, an imagecataloging application, and the like.

Text system 118 also includes assets 168. Assets 168 can include anysuitable asset used by text system 118, such as pre-trained textrecognition systems provided by server 108, training databases,combinations thereof, and the like. Text system 118 also includes a copyof text recognition system 110 of server 108. Hence, though in theexample of FIG. 1 text recognition system 110 of server 108 is describedas training a text recognition system and providing it to one ofcomputing devices 104, computing devices 104 can additionally oralternatively train a text recognition system. A text recognition systemtrained by text system 118 (e.g., using a copy of text recognitionsystem 110), can be stored in assets 168 and made available to anysuitable application, such as detection application 166. In one example,one of computing devices 104 trains a text recognition system using textsystem 118, and provides the trained text recognition system to anotherdevice of computing devices 104. For instance, computing device 104-1may train a text recognition system and provide it to computing device104-2 to be used to recognize text in images.

Having considered an example digital medium environment, consider now adiscussion of example systems in accordance with one or more aspects ofthe disclosure.

Example Text Recognition Systems

FIG. 4 illustrates an example system 400 in accordance with one or moreaspects of the disclosure. In this implementation, system 400 includesthe modules of text recognition application 112 as described in FIG. 1 ,e.g., noisy image module 148, clean image module 150, feature extractionmodule 152, text prediction module 154, image generator module 156,feature discriminator module 158, image discriminator module 160, andtraining module 162. System 400 is one example of text recognitionsystem 110 that can be constructed using the modules of text recognitionapplication 112. For instance, signals can be redefined, and modules canbe modified, combined, divided, added, or removed to form a modifiedsystem, without altering the functionality of system 400. Accordingly,such modified systems are considered to be within the scope of thedisclosure.

Furthermore, for simplicity system 400 is limited to the modules of textrecognition application 112 and a description of some of theirinterconnects. System 400 can, however, include any suitable signals andcommunications between modules omitted for simplicity. Such signals mayinclude system clocks, counters, image indicators, feature mapindicators, image identification numbers, reset signals, and the like.In one example, system 400 can operate in real time (e.g., with noperceptible delay to a user) to generate a text prediction given aninput image. Accordingly, signals can be calculated by the modules ofsystem 400 and communicated between the modules of system 400 withoutsignificant delay. In one example, system 400 trains modules of system400 to recognize text in images. Additionally or alternatively, system400 can generate detection results including a text prediction for auser-provided input image, such as an image that is not part of atraining dataset used to train modules of system 400.

Moreover, system 400 can be implemented on any suitable device ordevices. In one example, system 400 is implemented on one computingdevice (e.g., server 108 or one of computing devices 104 in FIG. 1 ). Inanother example, system 400 is implemented on more than one computingdevice. For instance, parts of system 400 can be implemented by a firstcomputing device, such as computing device 104-1 or server 108 in FIG. 1, and other parts of system 400 can be implemented by an additionalcomputing device or devices, such as computing device 104-2. In oneexample, a server implements parts of system 400, such as server 108 inFIG. 1 . A server can be remote, e.g., because it is not collocated withthe first computing device. A server may be configured to receivesignals of system 400 from a computing device (e.g., one or more ofcomputing devices 104), process the received signals, such as with textrecognition system 110, and transmit results of the processing back tothe computing device. Hence, text recognition system 110 of server 108in FIG. 1 may include system 400. In one example, system 400 is used totrain an adaptive model (e.g., neural network, machine learning model,and the like) of a text recognition system by a first computing device,such as by server 108, and the trained adaptive model is supplied by thefirst computing device to a different computing device, such as one ofcomputing devices 104. For instance, server 108 may provide apre-trained feature encoder, E, and pre-trained text decoder, T, to oneof computing devices 104.

Noisy image module 148 obtains a noisy image, x. Noisy image module 148can obtain a noisy image in any suitable way. In one example, noisyimage module 148 synthesizes a noisy image with an image renderer toinclude text of a ground truth text string, y. For instance, noisy imagemodule 148 may synthesize a noisy image according to renderingparameters, including canonical parameters that designate how to rendertext without nuisance factors (e.g., according to a font style andsize), and nuisance factors that designate distortions and imperfectionsthat are applied to text of a noisy image, such as compressionartifacts, geometric distortion (e.g., warping), shadows, noise, and thelike.

Additionally or alternatively, noisy image module 148 may obtain aplurality of images from a database of images, such as a databaseincluding one or more training datasets that can be used to train a textrecognition system, such as system 400, to recognize text in images. Forinstance, noisy image module 148 may obtain images of a training datasetthat include text perturbed by nuisance factors. In one example, noisyimage module 148 obtains images that are not part of a training dataset,such as an image captured by a camera of a vehicle that is supplied asinput to a text recognition system of an autonomous guidance system forthe vehicle.

Noisy image module 148 provides a noisy image, x to feature extractionmodule 152. An example noisy image is illustrated in FIG. 4 at image402. Image 402 includes the text “ANODIZING” that has been distorted byone or more nuisance factors, including a rotation and a distortion ofthe text, and an additive background.

Clean image module 150 obtains a clean image, Y. Clean image module 150can obtain any suitable clean image in any suitable way. In one example,clean image module 150 renders a clean image using an image renderedaccording to a ground truth text string, y that is also used to render anoisy image with noisy image module 148. A clean image may be renderedwithout nuisance factors, such as based on canonical parameters withnuisance factors zeroed.

Additionally or alternatively, a clean image obtained by clean imagemodule 150 may include one or more nuisance factors. For instance, cleanimage module 150 may obtain a clean image by removing one or morenuisance factors from a noisy image generated by noisy image module 148,so that the clean image may include one or more nuisance factors of thenoisy image that are not removed in the clean image. In one example,clean image module 150 removes all nuisance factors from a noisy imageto generate a clean image without nuisance factors.

Additionally or alternatively, clean image module 150 can obtainrendering parameters used to render a noisy image by noisy image module148, and synthesize a clean image according to the rendering parametersthat corresponds to the noisy image. For instance, clean image module150 may remove, zero, or otherwise disable one or more renderingparameters used to render a noisy image to render a clean imagecorresponding to the noisy image.

Clean image module 150 provides a clean image, x, to feature extractionmodule 152 and image discriminator module 160. An example clean image isillustrated in FIG. 4 at image 404. Image 404 includes the text“ANODIZING” that has rendered without nuisance factors, so that the textis not distorted.

Feature extraction module 152 receives a noisy image, x, from noisyimage module 148 and a clean image, x, from clean image module 150.Feature extraction module 152 extracts features from an image into afeature map. For instance, feature extraction module 152 may include afeature encoder, E, to extract features from an image into a featuremap. In the example in FIG. 4 , feature extraction module 152 extractsfeatures from a noisy image into a first feature map, f, and featuresfrom a clean image into a second feature map, f.

Feature extraction module 152 provides a first feature map, f, extractedfrom a noisy image a second feature map, f, extracted from a clean imageto feature discriminator module 158. Feature extraction module 152 alsoprovides a first feature map, f, extracted from a noisy image to textprediction module 154 and image generator module 156.

Text prediction module 154 receives a first feature map, f, extractedfrom a noisy image from feature extraction module 152, and generates atext prediction, ŷ, of the ground truth text, y. Text prediction module154 can generate a text prediction in any suitable way. In one example,text prediction module 154 includes a text decoder, T, that generates aprediction of text, such as decoder of a deep neural network configuredas an encoder-decoder structure.

In one example, text prediction module 154 receives a sequence offeature frames of a feature map from feature extraction module 152, andpredicts a sequence of probability distributions for the feature framesover a probability space, such as a probability space of allalphanumeric characters of a language and a blank token for wordseparation. Text prediction module 154 may translate the sequence ofprobability distributions into a text prediction, ŷ, through beamsearch.

Text prediction module 154 provides a text prediction, ŷ, to trainingmodule 162. In the example in FIG. 4 , a text prediction generated bytext prediction module 154 corresponding to the noisy image denoted byimage 402 may include the text “ANODIZING” (not shown in FIG. 4 ).

Image generator module 156 receives a first feature map, f, extractedfrom a noisy image from feature extraction module 152, and generates areconstructed clean image, {circumflex over (x)}, from the first featuremap. In one example, image generator module 156 includes an imagegenerator, G, that generates an image from a feature map, such asdeconvolutional neural network.

Image generator module 156 provides a reconstructed clean image,{circumflex over (x)}, to image discriminator module 160. An examplereconstructed clean image is illustrated in FIG. 4 at image 406. Image406 includes the text “ANODIZING” that has been generated from featuresextracted from noisy image, x.

Feature discriminator module 158 receives a first feature map, f,extracted from a noisy image a second feature map, f, extracted from aclean image. For each feature map provided to feature discriminatormodule 158, feature discriminator module 158 generates a feature binarylabel that classifies the input feature map as belonging to one of anoisy image or a clean image. For instance, feature discriminator module158 includes a feature discriminator, D_(F), that is adversariallytrained against a feature encoder, E, of feature extraction module 152in a generative adversarial manner. Feature discriminator module 158provides respective feature binary labels for first feature map andsecond feature map inputs to training module 162.

Image discriminator module 160 receives a clean image, x, from cleanimage module 150 and a reconstructed clean image, {circumflex over (x)},from image generator module 156. For each image provided to imagediscriminator module 160, image discriminator module 160 generates animage binary label that classifies the input image as one of a cleanimage or a reconstructed clean image. For instance, image discriminatormodule 160 includes an image discriminator, D_(I), that is adversariallytrained against a feature encoder, E, of feature extraction module 152and an image generator, G, in a conditional generative adversarialmanner. Image discriminator module 160 provides respective image binarylabels for clean image and reconstructed clean image inputs to trainingmodule 162.

Training module 162 receives respective feature binary labels for afirst feature map and a second feature map from feature discriminatormodule 158, and respective image binary labels for a clean image and areconstructed clean image from image discriminator module 160. Trainingmodule 162 also receives a prediction of text, y, from text predictionmodule 154. Training module 162 also receives data for training. Datafor training can include any suitable data to train a text recognitionsystem, including data used by or calculated by system 400, such asfeature maps generated by feature extraction module 152, a clean imagesynthesized by clean image module 150, a reconstructed clean imagegenerated by image generator module 156, a noisy image obtained by noisyimage module 148, a ground truth text string (e.g., a ground truth textstring included in metadata of a noisy image obtained by noisy imagemodule 148), and the like.

Training module 162 evaluates a loss function using any suitable data,such as data for training used by or calculated by system 400. Trainingmodule 162 can evaluate any suitable loss term, such as overall lossterm,

, described above, using any suitable values for combining weights λ.For instance, training module 162 may evaluate a loss term including aweighted sum of a feature-matching loss term determined from thedifference of a first feature map and a second feature map, a featureadversarial loss term determined from respective binary labels for thefirst feature map and the second feature map, an image-reconstructionloss term determined from the difference of the reconstructed cleanimage and the clean image, an image adversarial loss term determinedfrom additional respective binary labels for the reconstructed cleanimage and the clean image, and a training loss term determined from aconditional probability of the ground truth text string given theprediction of the text. Additionally or alternatively, one or more ofthese loss terms may be zeroed when forming a loss function, such as bysetting a combining weight to zero.

Based on evaluating a loss function, training module 162 generatestraining updates. Training module 162 can generate training updatesaccording to a loss function in any suitable way. In one example,training module 162 generates training updates by stochastic gradientdescent of a loss function. Training updates generated by trainingmodule 162 are used by modules of system 400 to update the modules inany suitable way, such as by adjusting coefficients in a neural network.

Training module 162 provides training updates to any suitable module ofsystem 400. In one example, training module 162 provides trainingupdates to a feature encoder, E, of feature extraction module 152 and animage generator, G, of image generator module 156, a text decoder, T, oftext prediction module 154, a feature discriminator, D_(F), of featurediscriminator module 158, and an image discriminator, D_(I), of imagediscriminator module 160.

The systems described herein constitute an improvement over systems thatdo not train text recognition systems using clean images as conditionalinputs. Rather, the systems described herein include text recognitionsystems that are trained using both noisy images of text and acorresponding clean image. A clean image acts as supervision at bothfeature and pixel levels, so that a text recognition system is trainedto be feature invariant (e.g., by requiring features extracted from anoisy image to match features extracted from a clean image), and featurecomplete (e.g., by requiring that features extracted from a noisy imagebe sufficient to generate a clean image). At the feature level, afeature discriminator is adversarially trained against a featureencoder. At the pixel level, an image discriminator is adversariallytrained against a feature encoder and image generator. The imagediscriminator generates a confidence score that indicates a quality of atext prediction across a dimension (e.g., horizontally) of theprediction of the text, and can be used to detect and correct errors ina prediction of text.

Accordingly, text recognition systems trained by the systems describedherein are not limited to recognizing text in an image that correspondsto text of a training image, but can generalize to text not included intraining images, since the text recognition systems are trained to befeature complete. Moreover, text recognition systems trained by thesystems described herein are robust to nuisance factors, since the textrecognition systems are trained to be feature invariant. Furthermore,since a clean image is provided as supervision at feature and pixellevels using adversarially-trained discriminators, a text recognitionsystem trained by the systems described herein can be trained usingfewer training images than text recognition systems that are not trainedwith a supervisory clean image, thus saving time and resources.

Having considered example systems, consider now a discussion of exampleprocedures for recognizing text in images in accordance with one or moreaspects of the disclosure.

Example Procedures

FIG. 5 illustrates an example procedure 500 for recognizing text inimages in accordance with one or more aspects of the disclosure. Aspectsof the procedure may be implemented in hardware, firmware, or software,or a combination thereof. The procedure is shown as a set of blocks thatspecify operations performed by one or more devices and are notnecessarily limited to the orders shown for performing the operations bythe respective blocks. In at least some aspects, the procedure may beperformed in a digital medium environment by a suitably configuredcomputing device, such as one or more of computing devices 104 or server108 of FIG. 1 that makes use of a text recognition system, such assystem 400 in FIG. 4 , or text recognition system 110 in FIG. 1 . A textrecognition system implementing procedure 500 may be an independentapplication that has been installed on the computing device, a servicehosted by a service provider that is accessible by the computing device,a plug-in module to the computing device, or combinations thereof.

A noisy image including text is obtained (block 502). In one example,noisy image module 148 obtains a noisy image. Additionally oralternatively, the noisy image can be obtained by synthesizing the noisyimage according to one or more nuisance factors based on a ground truthtext string.

A clean image including the text is determined (block 504). In oneexample, clean image module 150 determines a clean image. Additionallyor alternatively, the clean image can be determined by removing at leastone of the one or more nuisance factors from the noisy image to form theclean image. In one example, the clean image is obtained by synthesizingthe clean image without nuisance factors based on a ground truth textstring.

A prediction of the text is generated by a text recognition system fromthe noisy image (block 506). In one example, feature extraction module152 and text prediction module 154 are included in the text recognitionsystem and generate a prediction of the text from the noisy image. Forinstance, feature extraction module 152 may provide a sequence offeature frames to text prediction module 154, which generates a textprediction from the sequence of feature frames.

The text recognition system is trained based on the prediction of thetext, the noisy image, and the clean image (block 508). In one example,training module 162, image generator module 156, image discriminatormodule 160, feature extraction module 152, and feature discriminatormodule 158 train the text recognition system based on the prediction ofthe text, the noisy image, and the clean image. For instance, trainingmodule 162 may jointly train these modules according to an overall loss

(x, x, y).

In one example, first features are extracted from the noisy image with afeature encoder of the text recognition system, and second features areextracted from the clean image with the feature encoder. The trainingincludes updating the feature encoder based on a feature-matching lossterm determined from a difference between the first features and thesecond features.

Additionally or alternatively, the updating includes providing the firstfeatures to a feature discriminator to assign a first binary label forthe first features, providing the second features to the featurediscriminator to assign a second binary label for the second features,forming a feature adversarial loss term based on the first binary labeland the second binary label, and training the feature encoderadversarially against the feature discriminator based on the featureadversarial loss term.

In one example, first features are extracted from the noisy image with afeature encoder of the text recognition system, and a reconstructedclean image is generated with an image generator from the firstfeatures. The training includes updating the feature encoder jointlywith the image generator based on an image-reconstruction loss termdetermined from a difference between the reconstructed clean image andthe clean image.

Additionally or alternatively, the updating includes providing thereconstructed clean image to an image discriminator to assign a firstbinary label for the reconstructed clean image, providing the cleanimage to the image discriminator to assign a second binary label for theclean image, forming an image adversarial loss term based on the firstbinary label and the second binary label, and training the featureencoder and the image generator adversarially against the imagediscriminator based on the image adversarial loss term.

In one example, features are extracted from the noisy image into afeature map with a feature encoder of the text recognition system, and atext decoder of the text recognition system generates the prediction ofthe text from the noisy image based on the feature map. The trainingincludes updating the feature encoder and the text decoder jointly basedon a training loss term determined from a conditional probability of aground truth text string given the prediction of the text.

Additionally or alternatively, at least part of the trained textrecognition system is provided to a different device than the computingdevice for recognizing a text occurrence in one or more images. Forinstance, a pre-trained feature encoder and a pre-trained text decodermay be provided from a server to a client device to use in the clientdevice to recognize text in images.

FIG. 6 illustrates an example procedure 600 for recognizing text inimages in accordance with one or more aspects of the disclosure. Aspectsof the procedure may be implemented in hardware, firmware, or software,or a combination thereof. The procedure is shown as a set of blocks thatspecify operations performed by one or more devices and are notnecessarily limited to the orders shown for performing the operations bythe respective blocks. In at least some aspects, the procedure may beperformed in a digital medium environment by a suitably configuredcomputing device, such as one or more of computing devices 104 or server108 of FIG. 1 that makes use of a text recognition system, such assystem 400 in FIG. 4 , or text recognition system 110 in FIG. 1 . A textrecognition system implementing procedure 600 may be an independentapplication that has been installed on the computing device, a servicehosted by a service provider that is accessible by the computing device,a plug-in module to the computing device, or combinations thereof.

A noisy image including text based on a ground truth text string isobtained (block 602). In one example, noisy image module 148 obtains anoisy image including text based on a ground truth text string. Forinstance, noisy image module 148 may synthesize a noisy image to includetext of a ground truth text string and add nuisance factors to the noisyimage, such as compression artifacts and sensor noise.

A clean image including the text is determined (block 604). In oneexample, clean image module 150 determines a clean image including thetext. For instance, clean image module 150 may obtain a clean image froma database of images, or synthesize a clean image.

In one example, the noisy image is obtained by synthesizing the noisyimage according to one or more nuisance factors based on the groundtruth text string, and the clean image is determined by removing atleast one of the one or more nuisance factors from the noisy image toform the clean image, or synthesizing the clean image without nuisancefactors based on the ground truth text string in a standard font.

Features of the noisy image are extracted into a feature map (block606). In one example, feature extraction module 152 extracts features ofthe noisy image into a feature map. Feature extraction module 152 mayflatten a feature map to generate a sequence of feature frames. Forinstance, a sequence of feature frames may be generated by flattening aplurality of feature segments sliced from a feature map horizontally(e.g., from left to right).

A prediction of the text is generated based on the feature map (block608). In one example, text prediction module 154 generates a predictionof the text based on the feature map. Text prediction module 154 maygenerate a prediction of text a sequence of feature frames of a featuremap.

The feature extraction module and the text prediction module are trainedto recognize image text based on the prediction of the text, the groundtruth text string, the clean image, and the noisy image (block 610). Inone example, training module 162, image generator module 156, imagediscriminator module 160, feature extraction module 152, and featurediscriminator module 158 train the feature extraction module and thetext prediction module to recognize image text based on the predictionof the text, the ground truth text string, the clean image, and thenoisy image.

In one example, the feature extraction module extracts additionalfeatures from the clean image, and to train the feature extractionmodule includes updating a feature encoder based on a feature-matchingloss term determined from a difference between the features and theadditional features.

Additionally or alternatively, a feature discriminator module canreceive the features and the additional features, and assign respectivebinary labels for the features and the additional features. The trainingmodule can be configured to train the feature extraction moduleadversarially against the feature discriminator module based on afeature adversarial loss term determined from the respective binarylabels.

In one example, an image generator module is configured to generate areconstructed clean image from the feature map, and the training moduleis configured to update the feature extraction module jointly with theimage generator module based on an image-reconstruction loss termdetermined from a difference between the reconstructed clean image andthe clean image.

Additionally or alternatively, an image discriminator module isconfigured to receive the reconstructed clean image and the clean image,and assign respective binary labels for the reconstructed clean imageand the clean image. The training module can be configured to train thefeature extraction module and the image generator module adversariallyagainst the image discriminator module based on an image adversarialloss term determined from the respective binary labels. In one example,the image discriminator module generates a confidence score from theimage adversarial loss term that indicates a quality of the predictionof the text across a dimension of the prediction of the text.

In one example, to train the feature extraction module and the textprediction module includes updating the feature extraction module andthe text prediction module jointly based on a training loss termdetermined from a conditional probability of the ground truth textstring given the prediction of the text.

FIG. 7 illustrates an example procedure 700 for recognizing text inimages in accordance with one or more aspects of the disclosure. Aspectsof the procedure may be implemented in hardware, firmware, or software,or a combination thereof. The procedure is shown as a set of blocks thatspecify operations performed by one or more devices and are notnecessarily limited to the orders shown for performing the operations bythe respective blocks. In at least some aspects, the procedure may beperformed in a digital medium environment by a suitably configuredcomputing device, such as one or more of computing devices 104 or server108 of FIG. 1 that makes use of a text recognition system, such assystem 400 in FIG. 4 , or text recognition system 110 in FIG. 1 . A textrecognition system implementing procedure 700 may be an independentapplication that has been installed on the computing device, a servicehosted by a service provider that is accessible by the computing device,a plug-in module to the computing device, or combinations thereof.

A noisy image including text is rendered based on a ground truth textstring and at least one nuisance factor (block 702). In one example,noisy image module 148 renders a noisy image including text based on aground truth text string and at least one nuisance factor.

A clean image without nuisance factors including the text is synthesizedbased on the ground truth text string (block 704). In one example, cleanimage module 150 synthesizes without nuisance factors a clean imageincluding the text based on the ground truth text string.

Features of the noisy image are extracted into a first feature map andfeatures of the clean image are extracted into a second feature map(block 706). In one example, feature extraction module 152 extractsfeatures of the noisy image into a first feature map and features of theclean image into a second feature map.

A reconstructed clean image is generated from the first feature map(block 708). In one example, image generator module 156 generates areconstructed clean image from the first feature map.

A prediction of the text is generated with a text recognition systembased on the first feature map (block 710). In one example, textprediction module 154 is included in a text recognition system andgenerates a prediction of the text based on the first feature map.

The text recognition system is trained based on the prediction of thetext, the ground truth text string, a difference of the reconstructedclean image and the clean image, and a difference of the first featuremap and the second feature map (block 712). In one example, trainingmodule 162, image discriminator module 160, and feature discriminatormodule 158 train the text recognition system based on the prediction ofthe text, the ground truth text string, a difference of thereconstructed clean image and the clean image, and a difference of thefirst feature map and the second feature map.

Additionally or alternatively, training the text recognition system canbe based on a loss constructed from a weighted sum of a feature-matchingloss term determined from the difference of the first feature map andthe second feature map, a feature adversarial loss term determined fromrespective binary labels for the first feature map and the secondfeature map, an image-reconstruction loss term determined from thedifference of the reconstructed clean image and the clean image, animage adversarial loss term determined from additional respective binarylabels for the reconstructed clean image and the clean image, and atraining loss term determined from a conditional probability of theground truth text string given the prediction of the text.

The procedures described herein constitute an improvement overprocedures that do not train text recognition systems using clean imagesas conditional inputs. Rather, the procedures described herein traintext recognition systems using both noisy images of text and acorresponding clean image. A clean image acts as supervision at bothfeature and pixel levels, so that a text recognition system is trainedto be feature invariant (e.g., by requiring features extracted from anoisy image to match features extracted from a clean image), and featurecomplete (e.g., by requiring that features extracted from a noisy imagebe sufficient to generate a clean image). At the feature level, afeature discriminator is adversarially trained against a featureencoder. At the pixel level, an image discriminator is adversariallytrained against a feature encoder and image generator. The imagediscriminator generates a confidence score that indicates a quality oftext prediction across a dimension (e.g., horizontally) of theprediction of the text, and can be used to detect and correct errors ina prediction of text.

Accordingly, text recognition systems trained by the proceduresdescribed herein are not limited to recognizing text in an image thatcorresponds to text of a training image, but can generalize to text notincluded in training images, since the text recognition systems aretrained to be feature complete. Moreover, text recognition systemstrained by the procedures described herein are robust to nuisancefactors, since the text recognition systems are trained to be featureinvariant. Furthermore, since a clean image is provided as supervisionat feature and pixel levels using adversarially-trained discriminators,a text recognition system trained by the procedures described herein canbe trained using fewer training images than text recognition systemsthat are not trained with a supervisory clean image, thus saving timeand resources.

Having considered example procedures in accordance with one or moreimplementations, consider now example systems and devices that can beutilized to practice the inventive principles described herein.

Example Systems and Devices

FIG. 8 illustrates an example system generally at 800 that includesexample computing devices 802-1, 802-2, 802-3, 802-4, 802-5, and 802-6(collectively 802) that is representative of one or more computingsystems and devices that may implement the various techniques describedherein. Computing devices 802 can be any suitable computing device(e.g., user computing devices). Computing devices 802 may be, forexample, a user computing device (e.g., one of computing devices 104),or a server device, (e.g., server 108). Furthermore, computing device802 may include an on-chip system, multiple computing devices,combinations thereof, or any other suitable computing device orcomputing system. Accordingly, FIG. 8 illustrates computing devices 802as one or more of a tablet, a laptop computer, a smart phone, smart eyeglasses, and a drone (e.g., a computing device of a drone), though theseexamples are illustrative and in no way are meant to limit the type ornumber of devices included in computing device 802.

Furthermore, computing devices 802 are coupled to “cloud” 804 includingplatform 806 that is representative of one or more computing systems anddevices that may implement the various techniques described herein, suchas servers, edge servers, global servers, or combinations thereof. Thisis illustrated through inclusion of text recognition application 112,detection application 166, text recognition system 110, text system 118,server 108, and system 400, in modules of platform 806, which operate asdescribed above.

Functionality of computing devices 802 may be implemented all or in partthrough use of a distributed system, such as over a “cloud” 804 via aplatform 806. Furthermore, platform 806 may host data accessible bycomputing devices 802, and therefore computing devices 802 may berequired to be authenticated to platform 806.

Platform 806 includes a processing system 808, one or morecomputer-readable media 810, and one or more I/O interfaces 812 that arecommunicatively coupled to each other. Although not shown, platform 806may further include a system bus or other data and command transfersystem that couples the various components, one to another. A system buscan include any one or combination of different bus structures, such asa memory bus or memory controller, a peripheral bus, a universal serialbus, and a processor or local bus that utilizes any of a variety of busarchitectures. A variety of other examples are also contemplated, suchas control and data lines.

Processing system 808 is representative of functionality to perform oneor more operations using hardware. Accordingly, processing system 808 isillustrated as including hardware elements 814 that may be configured asprocessors, functional blocks, and so forth. This may includeimplementation in hardware as an application specific integrated circuitor other logic device formed using one or more semiconductors. Hardwareelements 814 are not limited by the materials from which they are formedor the processing mechanisms employed therein. For example, processorsmay be comprised of semiconductor(s) and transistors (e.g., electronicintegrated circuits (ICs)). In such a context, processor-executableinstructions may be electronically-executable instructions. Processors126 in FIG. 1 are examples of processing system 808.

Computer-readable media 810 (e.g., computer-readable storage media) isillustrated as including memory/storage 816. Storage 128 in FIG. 1 is anexample of memory/storage included in memory/storage 816. Memory/storagecomponent 816 may include volatile media (such as random access memory(RAM)), nonvolatile media (such as read only memory (ROM), Flash memory,optical disks, magnetic disks, and so forth), or combinations thereof.Memory/storage component 816 may include fixed media (e.g., RAM, ROM, afixed hard drive, and so on) as well as removable media (e.g., Flashmemory, a removable hard drive, an optical disc, and so forth).Computer-readable media 810 may be configured in a variety of other waysas further described below.

Input/output interface(s) 812 are representative of functionality toallow a user (e.g., a system administrator of platform 806) to entercommands and information to platform 806, and also allow information tobe presented to the user and other components or devices using variousinput/output devices. Examples of input devices include a keyboard, acursor control device (e.g., a mouse), a microphone, an array ofmicrophones, a scanner, touch functionality (e.g., capacitive or othersensors that are configured to detect physical touch), a camera (e.g.,which may employ visible or non-visible wavelengths such as infraredfrequencies to recognize movement as gestures that do not involvetouch), and so forth. Examples of output devices include a displaydevice (e.g., a monitor or projector), speakers, a printer, a networkcard, tactile-response device, and so forth. Thus, platform 806 may beconfigured in a variety of ways as further described below to supportuser interaction.

Platform 806 also includes applications 818. Applications 818 arerepresentative of any suitable applications capable of running onplatform 806, and may include a web browser which is operable to accessvarious kinds of web-based resources (e.g., assets, media clips, images,content, configuration files, services, user profiles, advertisements,coupons, and the like. Applications 818 include text recognitionapplication 112 and detection application 166, as previously described.Furthermore, applications 818 includes any applications supporting textrecognition system 110, text system 118, or system 400.

Cloud 804 includes and is representative of a platform 806. Platform 806abstracts underlying functionality of hardware (e.g., servers) andsoftware resources of cloud 804, and includes resources 820. Resources820 may include applications, data, services, and content that can beutilized while computer processing is executed on servers that areremote from computing devices 802. Resources 820 can also includeservices provided over the Internet, through a subscriber network, suchas a cellular or Wi-Fi network, or combinations thereof.

Resources 820 include recognition system store 822, which operates toprovide one or more pre-trained text recognition systems to one ofcomputing devices 802. Resources 820 also includes training datasetstore 824, which operates to provide one or more training datasets ofimages that can be used to train a text recognition system as describedherein.

Various techniques may be described herein in the general context ofsoftware, hardware elements, or program modules. Generally, such modulesinclude routines, programs, objects, elements, components, datastructures, and so forth that perform particular tasks or implementparticular abstract data types. The terms “module,” “functionality,” and“component” as used herein generally represent software, firmware,hardware, or a combination thereof. The features of the techniquesdescribed herein are platform-independent, meaning that the techniquesmay be implemented on a variety of commercial computing platforms havinga variety of processors.

An implementation of the described modules and techniques may be storedon or transmitted across some form of computer-readable media. Thecomputer-readable media may include a variety of media that may beaccessed by platform 806. By way of example, and not limitation,computer-readable media may include “computer-readable storage media”and “computer-readable signal media.”

“Computer-readable storage media” refers to media, devices, orcombinations thereof that enable persistent or non-transitory storage ofinformation in contrast to mere signal transmission, carrier waves, orsignals per se. Thus, computer-readable storage media does not includesignals per se or signal bearing media. The computer-readable storagemedia includes hardware such as volatile and non-volatile, removable andnon-removable media, storage devices, or combinations thereofimplemented in a method or technology suitable for storage ofinformation such as computer readable instructions, data structures,program modules, logic elements/circuits, or other data. Examples ofcomputer-readable storage media may include, but are not limited to,RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM,digital versatile disks (DVD) or other optical storage, hard disks,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or other storage device, tangible media, orarticle of manufacture suitable to store the desired information andwhich may be accessed by a computer.

“Computer-readable signal media” refers to a signal-bearing medium thatis configured to transmit instructions to the hardware of the platform806, such as via a network. Signal media typically may embody computerreadable instructions, data structures, program modules, or other datain a modulated data signal, such as carrier waves, data signals, orother transport mechanism. Signal media also include any informationdelivery media. The term “modulated data signal” means a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in the signal. By way of example, and not limitation,communication media include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared, and other wireless media.

As previously described, hardware elements 814 and computer-readablemedia 810 are representative of modules, programmable device logic,fixed device logic implemented in a hardware form, or combinationsthereof that may be employed in some aspects to implement at least someaspects of the techniques described herein, such as to perform one ormore instructions. Hardware may include components of an integratedcircuit or on-chip system, an application-specific integrated circuit(ASIC), a field-programmable gate array (FPGA), a complex programmablelogic device (CPLD), and other implementations in silicon or otherhardware. In this context, hardware may operate as a processing devicethat performs program tasks defined by instructions, logic embodied bythe hardware, or combinations thereof, as well as a hardware utilized tostore instructions for execution, e.g., the computer-readable storagemedia described previously.

Combinations of the foregoing may also be employed to implement varioustechniques described herein. Accordingly, software, hardware, orexecutable modules may be implemented as one or more instructions, logicembodied on some form of computer-readable storage media or by one ormore hardware elements 814, or combinations thereof. Platform 806 may beconfigured to implement particular instructions and functionscorresponding to the software and hardware modules. Accordingly,implementation of a module that is executable by platform 806 assoftware may be achieved at least partially in hardware, e.g., throughuse of computer-readable storage media and hardware elements 814 ofprocessing system 808. The instructions and functions may beexecutable/operable by one or more articles of manufacture (for example,processing system 808) to implement techniques, modules, and examplesdescribed herein.

CONCLUSION

In one or more implementations, a digital medium environment includes atleast one computing device. Systems, techniques, and devices aredescribed herein for training a text recognition system to recognizetext in images using both noisy images of text that have nuisancefactors applied, and a corresponding clean image (e.g., without nuisancefactors). A clean image acts as supervision at both feature and pixellevels, so that a text recognition system is trained to be featureinvariant (e.g., by requiring features extracted from a noisy image tomatch features extracted from a clean image), and feature complete(e.g., by requiring that features extracted from a noisy image besufficient to generate a clean image). Accordingly, text recognitionsystems can generalize to text not included in training images, and arerobust to nuisance factors. Furthermore, since a clean image is providedas supervision at feature and pixel levels, training a text recognitionsystem requires fewer training images than text recognition systems thatare not trained with a supervisory clean image, thus saving time andresources.

Although the invention has been described in language specific tostructural features and methodological acts, it is to be understood thatthe invention defined in the appended claims is not necessarily limitedto the specific features or acts described. Rather, the specificfeatures and acts are disclosed as example forms of implementing theclaimed invention.

What is claimed is:
 1. In a digital medium environment to train a textrecognition system, a method implemented by a computing device, themethod comprising: receiving a noisy image including text having atleast one distortion artifact that alters a visual appearance of thetext; receiving an ideal text image having clean text corresponding tothe text of the noisy image; determining a reconstructed clean image byremoving one or more nuisance factors from the noisy image that are abasis of the at least one distortion artifact of the text in the noisyimage; generating a text prediction of the text in the noisy image; andtraining the text recognition system based on the text prediction, adifference between the reconstructed clean image without the one or morenuisance factors and the ideal text image, and a difference between thenoisy image and the ideal text image.
 2. The method as described inclaim 1, further comprising synthesizing the noisy image according toone or more of the nuisance factors based on the ideal text image. 3.The method as described in claim 1, further comprising synthesizing thereconstructed clean image without the one or more nuisance factors basedon the ideal text image.
 4. The method as described in claim 1, furthercomprising: extracting features from the noisy image with a featureencoder of the text recognition system; extracting additional featuresfrom the reconstructed clean image with the feature encoder; and whereinthe training the text recognition system includes updating the featureencoder based on a feature-matching loss term determined from adifference between the features and the additional features.
 5. Themethod as described in claim 4, wherein the updating the feature encodercomprises: assigning a first binary label of the features by a featurediscriminator; assigning a second binary label of the additionalfeatures by the feature discriminator; forming a feature adversarialloss term based on the first binary label and the second binary label;and training the feature encoder adversarially against the featurediscriminator based on the feature adversarial loss term.
 6. The methodas described in claim 1, further comprising: extracting features fromthe noisy image with a feature encoder of the text recognition system;generating the reconstructed clean image from the extracted featureswith an image generator; and wherein the training the text recognitionsystem includes updating the feature encoder jointly with the imagegenerator based on an image-reconstruction loss term determined from thedifference between the reconstructed clean image and the ideal textimage.
 7. The method as described in claim 6, wherein the updating thefeature encoder comprises: assigning a first binary label for thereconstructed clean image by an image discriminator; assigning a secondbinary label for the ideal text image by the image discriminator;forming an image adversarial loss term based on the first binary labeland the second binary label; and training the feature encoder and theimage generator adversarially against the image discriminator based onthe image adversarial loss term.
 8. The method as described in claim 1,further comprising: extracting features from the noisy image into afeature map with a feature encoder of the text recognition system; andwherein the generating the text prediction by a text decoder of the textrecognition system is based on the feature map, and the training thetext recognition system includes updating the feature encoder and thetext decoder jointly based on a training loss term determined from aconditional probability of the ideal text image given the textprediction of the text in the noisy image.
 9. A text recognition systemimplemented at least partially in computer hardware in a digital mediumenvironment, the text recognition system comprising: a noisy imagemodule configured to receive a noisy image including text having atleast one distortion artifact that alters a visual appearance of thetext; a clean image module configured to determine a reconstructed cleanimage by removing one or more nuisance factors from the noisy image thatare a basis of the at least one distortion artifact of the text in thenoisy image; a text prediction module configured to generate a textprediction of the text in the noisy image; and a training moduleconfigured to train the text prediction module to recognize image textbased on the text prediction, a difference between the reconstructedclean image without the one or more nuisance factors and an ideal textimage, and a difference between the noisy image and the ideal textimage.
 10. The text recognition system as described in claim 9, wherein:the noisy image module is configured to synthesize the noisy imageaccording to one or more of the nuisance factors based on the ideal textimage, which has clean text corresponding to the text of the noisyimage; and the clean image module determines the reconstructed cleanimage by the clean image module configured to synthesize thereconstructed clean image without the one or more nuisance factors basedon the ideal text image in a standard font.
 11. The text recognitionsystem as described in claim 9, further comprising: a feature extractionmodule configured to extract features of the noisy image, and extractadditional features from the reconstructed clean image; and wherein thetraining module is configured to train the feature extraction module byupdating a feature encoder based on a feature-matching loss termdetermined from a difference between the features and the additionalfeatures.
 12. The text recognition system as described in claim 11,further comprising a feature discriminator module configured to: receivethe features extracted from the noisy image and the additional featuresextracted from the reconstructed clean image; and assign respectivebinary labels for the features and the additional features; and whereinthe training module is configured to train the feature extraction moduleadversarially against the feature discriminator module based on afeature adversarial loss term determined from the respective binarylabels.
 13. The text recognition system as described in claim 9, furthercomprising: a feature extraction module configured to extract featuresof the noisy image; and an image generator module configured to generatethe reconstructed clean image from the extracted features; and whereinthe training module is configured to update the feature extractionmodule jointly with the image generator module based on animage-reconstruction loss term determined from a difference between thereconstructed clean image and the ideal text image.
 14. The textrecognition system as described in claim 13, further comprising an imagediscriminator module configured to: receive the reconstructed cleanimage and the ideal text image; and assign respective binary labels forthe reconstructed clean image and the ideal text image; and wherein thetraining module is configured to train the feature extraction module andthe image generator module adversarially against the image discriminatormodule based on an image adversarial loss term determined from therespective binary labels.
 15. The text recognition system as describedin claim 14, wherein the image discriminator module is configured togenerate a confidence score from the image adversarial loss term thatindicates a quality of the text prediction of the text in the noisyimage.
 16. The text recognition system as described in claim 9, furthercomprising: a feature extraction module configured to extract featuresof the noisy image; and wherein the training module is configured totrain the feature extraction module and the text prediction modulejointly based on a training loss term determined from a conditionalprobability of the ideal text image given the text prediction of thetext in the noisy image.
 17. In a digital medium environment to train atext recognition system, a method implemented by a computing device, themethod comprising: receiving a noisy image including text having atleast one distortion artifact that alters a visual appearance of thetext; determining a reconstructed clean image by removing one or morenuisance factors from the noisy image that are a basis of the at leastone distortion artifact of the text in the noisy image; extractingfeatures from the noisy image and additional features from thereconstructed clean image; and training the text recognition systembased on a text prediction of the text in the noisy image, and based ona feature-matching loss term determined from a difference between theextracted features from the noisy image and the additional extractedfeatures from the reconstructed clean image without the one or morenuisance factors.
 18. The method as described in claim 17, furthercomprising: receiving an ideal text image having clean textcorresponding to the text of the noisy image; synthesizing the noisyimage according to one or more of the nuisance factors based on theideal text image; and synthesizing the reconstructed clean image withoutthe one or more nuisance factors based on the ideal text image.
 19. Themethod as described in claim 17, further comprising: generating aconfidence score that indicates a quality of the text prediction of thetext in the noisy image.
 20. The method as described in claim 17,wherein the training the text recognition system is further based on aloss constructed from a weighted sum of: the feature-matching loss term;a feature adversarial loss term determined from respective binary labelsfor the extracted features from the noisy image and the additionalextracted features from the reconstructed clean image; animage-reconstruction loss term determined from a difference between thereconstructed clean image and an ideal text image; an image adversarialloss term determined from additional respective binary labels for thereconstructed clean image and the ideal text image; and a training lossterm determined from a conditional probability of the ideal text imagegiven the text prediction of the text in the noisy image.